Benchmark: flatMap vs reduce (group count)

Script Preparation code:

var arr = Array(10).fill({items: Array(1000).fill(0)})

Tests:

reduce with concat
arr.reduce((acc, x) => acc + x.length)
flatMap
arr.flatMap(x => x.items).length

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
reduce with concat
flatMap

Fastest: N/A

Slowest: N/A

Latest run results:

Run details: (Test run date: one year ago)

User agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36 Edg/131.0.0.0

Browser/OS: Chrome 131 on Windows

View result in a separate tab

Test name	Executions per second
reduce with concat	9139557.0 Ops/sec
flatMap	9301.0 Ops/sec

Autogenerated LLM Summary (model gpt-4o-mini, generated one year ago):

LLMs can make mistakes. Check important info.

The benchmark you provided compares the performance of two JavaScript approaches for grouping and counting items within an array of objects: using `reduce` versus using `flatMap`.

### Benchmark Overview
- **Name:** flatMap vs reduce (group count)
- **Input Data:** An array of 10 objects, each containing a `items` key that maps to an array of 1000 zeros. This setup is meant to simulate a scenario where we're interested in counting the total number of items across all objects.

### Test Cases Detailed
1. **Test Case: reduce with concat**
   - **Benchmark Definition:** `arr.reduce((acc, x) => acc + x.length)`
   - **Purpose:** This test case uses the `reduce` method to accumulate the lengths of the objects within the array. It iteratively adds the `length` of each object in `arr` to a running total (`acc`).
   - **Pros:**
     - It is a functional programming approach that can be quite readable for those familiar with JavaScript's array methods.
     - Works with older versions of JavaScript, and compatibility is generally good.
   - **Cons:**
     - As the implementation relies on accumulating the length property, it may inherently create a new immutable array in the process, which could be less efficient than a direct approach.
     - The performance may degrade with larger datasets due to repeated addition operations.

2. **Test Case: flatMap**
   - **Benchmark Definition:** `arr.flatMap(x => x.items).length`
   - **Purpose:** This test case uses the `flatMap` method to first flatten the array of `items` and then retrieves the total count by checking the length of the flattened array. 
   - **Pros:**
     - `flatMap` combines mapping and flattening in a single method, making it a one-step process to achieve the desired structure.
     - It can provide better performance for large nested structures as it avoids the need to chunk results in multiple steps.
   - **Cons:**
     - It is relatively newer and might not be compatible with older JavaScript engines, specifically prior to ES2019.
     - Using `flatMap` can lead to increased memory usage depending on the size of the arrays being processed because it creates an intermediary flattened array.

### Benchmark Results
The benchmark results show a clear performance difference:
- **reduce with concat:** Achieved **8,358,828.5 executions per second**.
- **flatMap:** Achieved **10,361.7 executions per second**.

### Conclusion and Alternatives
In this benchmark, using `reduce` significantly outperformed `flatMap`. This suggests that while `flatMap` is a powerful and potentially easier-to-read method for flattening and mapping arrays, it may have some overhead that impacts performance in specific cases, particularly with simpler operations like counting lengths.

### Other Alternatives
Alternative approaches to achieve similar tasks could include:
- **Using a simple loop:** Iterating through the array to accumulate counts, which may even outperform both methods in some scenarios. 
- **Using `forEach` in conjunction with a manual accumulator**: Similar to `reduce`, but may allow for potentially clearer syntax at the cost of not being purely functional.
- **Using libraries:** Libraries like Lodash offer utility functions (`_.sumBy`) that can simplify and potentially optimize array operations but come with their own performance overhead.

Each of these alternatives varies in terms of readability, performance, and compatibility; thus, the choice can depend on specific use cases and coding standards in your application.

Related benchmarks:

flatMap vs reduce (group count) (version: 1)

Comparing performance of: reduce with concat vs flatMap

Created: one year ago by: Guest

Jump to the latest result

reduce with concat

flatMap

Suite status: <idle, ready to run>

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model gpt-4o-mini, generated one year ago):